Channel estimation in wireless communication provides important knowledge for various system functionalities, and allows data reception in coherent modulation through equalization. In low mobility systems such as the wireless local area network (WLAN) IEEE 802.11 family, channel estimates are conventionally obtained at the beginning of transmission using a specific preamble sequence. At low velocities channel variations are negligible, and so the preamble based channel estimate can be used for the remainder of the transmission time. But this sets boundaries to the maximum tolerable packet size, which can limit the overall throughput of the system.
In high mobility systems the validity of the channel estimate degrades rapidly as time proceeds, which can be overcome by transmitting reference data on frequent intervals and updating the channel during the whole transmission as new reference data becomes available. This technique has been approved for WLAN 802.11ah (draft specification), which terms this approach as a travelling pilot scheme. Until now the pilot carriers in the 802.11 family of radio specifications have used a constant location in frequency, so the travelling pilot procedure is a significant change taken by then 802.11ah group for WLAN systems. The travelling pilot scheme concept may also be utilized in the future High Efficiency WLAN (HEW) amendment following a recent amendment to IEEE 802.11ac.
Consider an orthogonal frequency division multiplexing (OFDM) system. So long as some of the subcarriers do not have reference data such as pilot tones, the missing OFDM subcarriers must be interpolated in the frequency domain. It is often desirable that the frequency response obtained with this interpolation is also smoothened using coarsely estimated channel impulse response characteristics. Both, interpolation and smoothing can be performed using a proper inverse Fast Fourier Transform (IFFT) and Fast Fourier Transform (FFT) processing.
FFT smoothing and corresponding IFFT/FFT interpolation are used to reduce noise in the estimated channel frequency response and possibly to interpolate missing subcarriers in the channel estimation. Such IFFT/FFT processing in the context of UTRAN (LTE) and Worldwide Interoperability for Microwave Access (WiMAX) systems at a paper by Fanghua Weng, Changchuan Yin and Tao Luo entitled “Channel estimation for the downlink of 3GPP-LTE systems,” Network Infrastructure and Digital Content, 2010 2nd IEEE International Conference on, vol., no., pp. 1042-1046, 24-26 Sep. 2010; and also in another paper by Y. Shen and E. F. Martinez entitled “WiMAX Channel Estimation: Algorithms and Implementations”, Application Notes, AN3429, Freescale Semiconductor Inc., July 2007, available at http://code.uesd.edu/˜yushen/publications.html (last visited Sep. 14, 2013).
The basic idea of the above two papers is to estimate the channel at those subcarrier indices where reference data is available, and then after taking the IFFT over the estimated subcarriers, the resulting impulse response is windowed depending on the estimated characteristics of the channel impulse response. Then finally there is taken a full band FFT of the windowed impulse response to increase the frequency resolution, and thus interpolate the missing subcarriers.
FFT processing is a vital part of the modulation and de-modulation techniques in OFDM systems. To reduce implementation complexity of channel estimation as above, compared to other frequency domain filtering techniques, the conventional FFT processor can be extended to support both the modulation techniques and the channel estimation. See for example Haene, S.; Burg, A., Luethi, P., Felber, N. and Fichtner, W.; “FFT Processor for OFDM Channel Estimation,” Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on, vol., no., pp. 1417-1420, 27-30 May 2007.
The approach described above assumes a suitable pilot tone resolution in the frequency domain. Channel estimation algorithms typically require receipt of a sufficient number of symbols, which depends on how dense are the pilot tones in the tone pattern, before the channel estimate can be calculated. Between these channel estimate intervals the most recent channel estimate is used, because the IFFT/FFT processing can be computationally too challenging to be performed for every received symbol. Also, if the frequency resolution of the pilots in one symbol is not enough, it is not possible to achieve proper symbol-wise channel estimates.
A different channel estimation technique uses a time domain interpolation, but this tends to introduce additional delays which are not tolerable in many cases. The computational complexity of time-frequency interpolation also increases considerably, especially with advanced interpolation methods such as Wiener filter based methods that are reviewed by Hoeher, P., Kaiser, S. and Robertson, P. in “Two-dimensional pilot-symbol-aided channel estimation by Wiener filtering,” Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on, vol. 3, no., pp. 1845-1848 vol. 3, 21-24 Apr. 1997; and also by Jinfeng Hou and Jian Liu in “A novel channel estimation algorithm for 3GPP LTE downlink system using joint time-frequency two-dimensional iterative Wiener filter,” Communication Technology (ICCT), 2010 12th IEEE International Conference on, vol., no., pp. 289-292, 11-14 Nov. 2010. The former of these two papers indicates that dividing two-dimensional (time-frequency) filters into two separate one-dimensional filters will only slightly decrease the performance.
The example implementations of these teachings that are detailed below focus on the case without the above mentioned time delay, and so at least those non-limiting embodiments time domain interpolation is not an option.